The t-SNE [1]} method is capable of identifying localized [2]} clusters in the data (e.g., differences in the Cyanobacteria; Fig. S4A) but cannot reliably capture global features of the dataset. This is because t-SNE minimizes an objective function that effectively ignores large dissimilarities between points, causing distances in t-SNE space to be mostly meaningless [3]}. This leads to issues particularly in the analysis of data that are well-described by continuous or branching trajectories (e.g., cell differentiation data), as t-SNE shatters these trajectories leading to the false impression of data clusters [3]}.
